热加工工艺
熱加工工藝
열가공공예
HOT WORKING TECHNOLOGY
2010年
1期
33-35,39
,共4页
苏建%赵志龙%艾昌辉%胡鹏
囌建%趙誌龍%艾昌輝%鬍鵬
소건%조지룡%애창휘%호붕
GRNN神经网络%高温合金%细晶铸造%细化剂
GRNN神經網絡%高溫閤金%細晶鑄造%細化劑
GRNN신경망락%고온합금%세정주조%세화제
generalized regression neural network (GR_NN)%superalloy%fine grain casting%refiner
对镍基高温合金铸件研制出四组元化学细化剂A-B-C-D,针对细化剂的优选问题,采用GRNN神经网络模拟细化剂各组元含量与铸件晶粒尺寸间的非线性关系.研究发现:加入A-B-C-D新型复合细化剂可以明显细化铸件晶粒;细化剂的最佳加入量为0.11wt%A、0.23wt%B、0.14wt%C、0.17wt%D,即组元A-B-C-D的最佳质量配比约为1:2.1:1.3:1.5.
對鎳基高溫閤金鑄件研製齣四組元化學細化劑A-B-C-D,針對細化劑的優選問題,採用GRNN神經網絡模擬細化劑各組元含量與鑄件晶粒呎吋間的非線性關繫.研究髮現:加入A-B-C-D新型複閤細化劑可以明顯細化鑄件晶粒;細化劑的最佳加入量為0.11wt%A、0.23wt%B、0.14wt%C、0.17wt%D,即組元A-B-C-D的最佳質量配比約為1:2.1:1.3:1.5.
대얼기고온합금주건연제출사조원화학세화제A-B-C-D,침대세화제적우선문제,채용GRNN신경망락모의세화제각조원함량여주건정립척촌간적비선성관계.연구발현:가입A-B-C-D신형복합세화제가이명현세화주건정립;세화제적최가가입량위0.11wt%A、0.23wt%B、0.14wt%C、0.17wt%D,즉조원A-B-C-D적최가질량배비약위1:2.1:1.3:1.5.
A new chemical refiner composed of A-B-C-D for Ni-based superalloy casting was developed. According to the optimization of refiner, the generalized regression neural network (GRNN) was adopted to simulate the nonlinear connection between each component content of refiner and casting grain size. The results show that the grain size of ingots can be refined obviously by adding new composite refiner A-B-C-D; the combination of 0.11wt%A,0.23wt%B,0.14wt%C and 0.17wt%D is best as composite refiner the optimum proportion of component A-B-C-D is about 1:2.1:1.3:1.5.
